statistics-0.16.3.0: A library of statistical types, data, and functions
Copyright(c) 2020 Ximin Luo
LicenseBSD3
Maintainerinfinity0@pwned.gg
Stabilityexperimental
Portabilityportable
Safe HaskellNone
LanguageHaskell2010

Statistics.Distribution.Lognormal

Contents

Description

The log normal distribution. This is a continuous probability distribution that describes data whose log is clustered around a mean. For example, the multiplicative product of many independent positive random variables.

Synopsis

Documentation

data LognormalDistribution #

The lognormal distribution.

Instances

Instances details
FromJSON LognormalDistribution # 
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ToJSON LognormalDistribution # 
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Binary LognormalDistribution # 
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Data LognormalDistribution # 
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Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> LognormalDistribution -> c LognormalDistribution #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c LognormalDistribution #

toConstr :: LognormalDistribution -> Constr #

dataTypeOf :: LognormalDistribution -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c LognormalDistribution) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c LognormalDistribution) #

gmapT :: (forall b. Data b => b -> b) -> LognormalDistribution -> LognormalDistribution #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> LognormalDistribution -> r #

gmapQr :: forall r r'. (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> LognormalDistribution -> r #

gmapQ :: (forall d. Data d => d -> u) -> LognormalDistribution -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> LognormalDistribution -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> LognormalDistribution -> m LognormalDistribution #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> LognormalDistribution -> m LognormalDistribution #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> LognormalDistribution -> m LognormalDistribution #

Generic LognormalDistribution # 
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Defined in Statistics.Distribution.Lognormal

Associated Types

type Rep LognormalDistribution 
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Defined in Statistics.Distribution.Lognormal

type Rep LognormalDistribution = D1 ('MetaData "LognormalDistribution" "Statistics.Distribution.Lognormal" "statistics-0.16.3.0-JhFVwN7Kv9y2XSvDlj54t" 'True) (C1 ('MetaCons "LND" 'PrefixI 'False) (S1 ('MetaSel ('Nothing :: Maybe Symbol) 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 NormalDistribution)))
Read LognormalDistribution # 
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Show LognormalDistribution # 
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Eq LognormalDistribution # 
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ContDistr LognormalDistribution # 
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ContGen LognormalDistribution # 
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Distribution LognormalDistribution # 
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Entropy LognormalDistribution # 
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MaybeEntropy LognormalDistribution # 
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MaybeMean LognormalDistribution # 
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MaybeVariance LognormalDistribution # 
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Mean LognormalDistribution # 
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Variance LognormalDistribution # 
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FromSample LognormalDistribution Double #

Variance is estimated using maximum likelihood method (biased estimation) over the log of the data.

Returns Nothing if sample contains less than one element or variance is zero (all elements are equal)

Instance details

Defined in Statistics.Distribution.Lognormal

type Rep LognormalDistribution # 
Instance details

Defined in Statistics.Distribution.Lognormal

type Rep LognormalDistribution = D1 ('MetaData "LognormalDistribution" "Statistics.Distribution.Lognormal" "statistics-0.16.3.0-JhFVwN7Kv9y2XSvDlj54t" 'True) (C1 ('MetaCons "LND" 'PrefixI 'False) (S1 ('MetaSel ('Nothing :: Maybe Symbol) 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 NormalDistribution)))

Constructors

lognormalDistr #

Arguments

:: Double

Mu

-> Double

Sigma

-> LognormalDistribution 

Create log normal distribution from parameters.

lognormalDistrErr #

Create log normal distribution from parameters.

lognormalDistrMeanStddevErr #

Create log normal distribution from mean and standard deviation.

lognormalStandard :: LognormalDistribution #

Standard log normal distribution with mu 0 and sigma 1.

Mean is sqrt e and variance is (e - 1) * e.